The Influence of Negative Emotions in an Online Brand Community on Customer Innovation Activities

With Web 2.0 increased user participation in diverse e-communities results in prevalence of information, including emotional information. We examined the influence of negative emotion in an online brand community, MyStarbuckIdea.com developed to collect diverse customer ideas for firm's innovation, with the purpose to investigate how such emotion affects customer innovation activities in the community. We first established several hypotheses on the relationships between discrete negative emotions and innovation activities. Then, having collected 84,918 customer ideas, we conducted POS tagging and term-based matching to calculate the inclusion and intensity of negative emotion, using negative emotion lexicon which we developed. As a result of testing hypotheses with regression models, we show that 1) negative emotion significantly affects innovation activities in the brand community and frustration is the most influential among the discrete negative emotions; 2) as the intensity level of negative emotions increases, so does their influence.

[1]  Susan T. Fiske,et al.  Attention and weight in person perception: The impact of negative and extreme behavior. , 1980 .

[2]  Daniele Dalli,et al.  Emotions that Drive Consumers Away from Brands: Measuring Negative Emotions Toward Brands and Their Behavioral Effects , 2012 .

[3]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[4]  Jeff T. Larsen,et al.  Negative information weighs more heavily on the brain: the negativity bias in evaluative categorizations. , 1998, Journal of personality and social psychology.

[5]  P. Ellsworth,et al.  Beyond simple pessimism: effects of sadness and anger on social perception. , 1993, Journal of personality and social psychology.

[6]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

[7]  Braja Gopal Patra,et al.  Part of Speech (POS) Tagger for Kokborok , 2012, COLING.

[8]  C. Osgood,et al.  The Pollyanna hypothesis. , 1969 .

[9]  Veronica Liljander,et al.  Emotions in service satisfaction , 1997 .

[10]  Mike Thelwall,et al.  Negative emotions boost user activity at BBC forum , 2010, 1011.5459.

[11]  R. Mizerski.  An Attribution Explanation of the Disproportionate Influence of Unfavorable Information , 1982 .

[12]  N. Miller,et al.  Frustration and aggression , 1939 .

[13]  Thomas C. O'Guinn,et al.  brand community , 2022, The Fairchild Books Dictionary of Fashion.

[14]  P. Kleinginna,et al.  A categorized list of emotion definitions, with suggestions for a consensual definition , 1981 .

[15]  R. Pieters,et al.  Angry customers don't come back, they get back: The experience and behavioral implications of anger and dissatisfaction in services , 2003 .

[16]  Thomas Gottron,et al.  Bad news travel fast: a content-based analysis of interestingness on Twitter , 2011, WebSci '11.

[17]  K. Menon,et al.  Scripting Consumer Emotions in Extended Service Transactions: a Prerequisite For Successful Adaptation in Provider Performance , 1999 .

[18]  Mike Thelwall,et al.  Sentiment in Twitter events , 2011, J. Assoc. Inf. Sci. Technol..

[19]  Sep Kamvar,et al.  We Feel Fine: An Almanac of Human Emotion , 2009 .

[20]  J. Lerner,et al.  Beyond valence: Toward a model of emotion-specific influences on judgement and choice , 2000 .

[21]  P. Andersen.  Relationship marketing and brand involvement of professionals through web-enhanced brand communities: the case of Coloplast , 2005 .

[22]  René Algesheimer,et al.  Interplay between Brand and Brand Community: Evidence from European Car Clubs , 2004 .

[23]  Pradeep Chintagunta,et al.  The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets , 2010, Mark. Sci..

[24]  Janyce Wiebe,et al.  RECOGNIZING STRONG AND WEAK OPINION CLAUSES , 2006, Comput. Intell..

[25]  J. McAlexander,et al.  Building Brand Community , 2002 .

[26]  Bing Liu,et al.  Sentiment Analysis and Opinion Mining , 2012, Synthesis Lectures on Human Language Technologies.

[27]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[28]  E. Diener,et al.  The personality structure of affect. , 1995 .

[29]  Bernd Stauß,et al.  Customer frustration in loyalty programs , 2005 .

[30]  R. Plutchik.  The emotions: Facts, theories and a new model. , 1964 .

[31]  Peter D. Turney,et al.  Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon , 2010, HLT-NAACL 2010.

[32]  Craig A. Smith,et al.  Patterns of cognitive appraisal in emotion. , 1985, Journal of personality and social psychology.

[33]  C. Izard.  The face of emotion , 1971 .

[34]  Andrea Esuli,et al.  SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining , 2006, LREC.

[35]  R. Plutchik.  Emotion, a psychoevolutionary synthesis , 1980 .

[36]  E. E. Jones,et al.  From Acts To Dispositions The Attribution Process In Person Perception1 , 1965 .

[37]  J. Marshall.  Open Innovation: The New Imperative for Creating and Profiting from Technology , 2004 .

[38]  R. Westbrook.  Product/Consumption-Based Affective Responses and Postpurchase Processes , 1987 .

[39]  L. Berkowitz.  Frustration-aggression hypothesis: examination and reformulation. , 1989, Psychological bulletin.

[40]  Bård Tronvoll,et al.  Negative emotions and their effect on customer complaint behaviour , 2011 .

[41]  Cane Leung,et al.  Sentiment Analysis of Product Reviews , 2009, Encyclopedia of Data Warehousing and Mining.

[42]  Victoria Lemieux,et al.  We Feel Fine , 2015 .